Introduction
A Fortune 500 retail company, with a global presence, struggled with data silos and slow decision-making due to fragmented and outdated data infrastructure. The company’s massive customer base and diverse product catalog required a robust data management system to optimize operations and improve customer experiences. However, legacy systems created bottlenecks, limiting scalability and agility.
Challenge
A leading telecom provider struggled with processing vast amounts of call data records, leading to network inefficiencies and inaccurate billing. Legacy systems couldn't handle the scale required for real-time analytics.
Solution
Deployed a real-time data pipeline using Apache Kafka and Spark, allowing for continuous processing of call data. Implemented an automated data quality framework to ensure accuracy in billing and customer analytics.
Outcome
Improved billing accuracy by 95%, enhanced customer insights, optimized network performance, and reduced operational costs.